System and method of dynamically modifying a spoken dialog system to reduce hardware requirements

ABSTRACT

A system and method for providing a scalable spoken dialog system are disclosed. The method comprises receiving information which may be internal to the system or external to the system and dynamically modifying at least one module within a spoken dialog system according to the received information. The modules may be one or more of an automatic speech recognition, natural language understanding, dialog management and text-to-speech module or engine. Dynamically modifying the module may improve hardware performance or improve a specific caller&#39;s speech processing accuracy, for example. The modification of the modules or hardware may also be based on an application or a task, or based on a current portion of a dialog.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to spoken dialog systems and morespecifically to a system and method for dynamically adjusting componentswithin a spoken dialog system according to network loads and otherrequirements to reduce hardware requirements.

2. Introduction

Any spoken dialog system, for example, to automate a call center, mustoperate on one or more compute devices such as a server. When a companydesires to implement a spoken dialog system, one component of planningand implementing the system is a prediction of the number of calls thatthe system should be able to manage simultaneously. As the number ofpredicted calls increases, then more computer resources must bepurchased and deployed to manage the load. The hardware costs ofimplementing such as system may make implementation of a spoken dialogsystem unaffordable.

Hardware costs in the initial acquisition and maintenance of a dialogsystem are an important consideration when planning call centerautomation with voice-enabled services. One common practice is to planfor assumed peak loads in terms of simultaneous users. Each user orcaller into the system may be assigned a port. Hardware planning basedon being able to manage peak loads is not cost efficient given that theaverage load might be significantly lower than the expected peak load.

What is needed in the art is a method for improving the efficiency ofthe use of computer resources when implementing a spoken dialog system.

SUMMARY OF THE INVENTION

Additional features and advantages of the invention will be set forth inthe description which follows, and in part will be obvious from thedescription, or may be learned by practice of the invention. Thefeatures and advantages of the invention may be realized and obtained bymeans of the instruments and combinations particularly pointed out inthe appended claims. These and other features of the present inventionwill become more fully apparent from the following description andappended claims, or may be learned by the practice of the invention asset forth herein.

The present invention addresses the problems in the prior art by makingthe various speech engines used in a spoken dialog system (engines ormodules for automatic speech recognition (ASR), text-to-speech (TTS),natural language understanding (NLU) and dialog manager (DM)) scalableand adaptable to the current load of the hardware the engines arerunning on. Dynamically adapting the speech engines to the existing userload allows for a much more efficient exploitation of available hardwareresources with the effect of lower hardware investments.

The invention comprises systems, methods and computer readable media forproviding a scalable spoken dialog system are disclosed. The methodcomprises receiving information which may be internal to the system orexternal to the system and dynamically modifying at least one modulewithin a spoken dialog system according to the received information. Themodules may be one or more modules such as an automatic speechrecognition, natural language understanding, dialog management andtext-to-speech module or engine. Dynamically modifying the module mayimprove hardware performance or improve a specific caller's speechprocessing accuracy, for example.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered to be limiting of its scope, the invention will bedescribed and explained with additional specificity and detail throughthe use of the accompanying drawings in which:

FIG. 1 illustrates the basic components of a spoken dialog system;

FIG. 2 illustrates an exemplary computer network upon which a spokendialog system is deployed; and

FIG. 3 illustrates a method embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Various embodiments of the invention are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the invention.

Spoken dialog systems aim to identify the intent of the person whoprovides a natural language input and take actions accordingly tosatisfy the requests. FIG. 1 is a functional block diagram of anexemplary natural language spoken dialog system 100. Natural languagespoken dialog system 100 may include an automatic speech recognition(ASR) module 102, a natural language understanding (NLU) module 104, adialog management (DM) module 106, a spoken language generation (SLG)module 108, and a text-to-speech (TTS) module 110.

ASR module 102 may analyze speech input and may provide a transcriptionof the speech input as output. SLU module 104 may receive thetranscribed input and may use a natural language understanding model toanalyze the group of words that are included in the transcribed input toderive a meaning from the input. The role of DM module 106 is tointeract in a natural way and help the user to achieve the task that thesystem is designed to support. DM module 106 may receive the meaning ofthe speech input from NLU module 104 and may determine an action, suchas, for example, providing a response, based on the input. SLG module108 may generate a transcription of one or more words in response to theaction provided by DM 106. TTS module 110 may receive the transcriptionas input and may provide generated audible speech as output based on thetranscribed speech.

Thus, the modules of system 100 may recognize speech input, such asspeech utterances, may transcribe the speech input, may identify (orunderstand) the meaning of the transcribed speech, may determine anappropriate response to the speech input, may generate text of theappropriate response and from that text, may generate audible “speech”from system 100, which the user then hears. In this manner, the user cancarry on a natural language dialog with system 100. Those of ordinaryskill in the art will understand the programming languages and means forgenerating and training ASR module 102 or any of the other modules inthe spoken dialog system. Further, the modules of system 100 may operateindependent of a full dialog system. For example, a computing devicesuch as a smartphone (or any processing device having a phonecapability) may have an ASR module wherein a user may say “call mom” andthe smartphone may act on the instruction without a “spoken dialog.”

A compute resource may be defined as any hardware resource such as acentral processing unit, harddisk space, memory, bandwidth for highspeed communication of data, random access memory and so forth. FIG. 2illustrates an exemplary processing system 200 comprising a plurality ofcompute devices such as computer servers 202, 204, 206, 208, 210, 212are networked together in a computer cluster or computer farm to managethe calls coming from users which one or more of the modules of system100 may be implemented. Each server will run one or more of the speechprocessing modules (ASR, NLU, DM, TTS). FIG. 2 illustrates variouscallers 1-6 and their ports connecting each caller to a speechprocessing server. As is known in the art, a server may include suchhardware elements as a bus, a processor or processors, memory, a readonly memory (ROM), a storage device, an input device, an output deviceand a communication interface. Bus may permit communication among thecomponents of system. As hardware components change and improve, theseelements may change without changing the principles of the invention.Since these are common hardware components, they are not shown in thefigures but are discussed herein.

A processor in the servers may include at least one conventionalprocessor or microprocessor that interprets and executes instructions.The memory may be a random access memory (RAM) or another type ofdynamic storage device that stores information and instructions forexecution by the processor. The memory may also store temporaryvariables or other intermediate information used during execution ofinstructions by processor. The ROM may include a conventional ROM deviceor another type of static storage device that stores static informationand instructions for processor. Storage device may include any type ofmedia, such as, for example, magnetic or optical recording media and itscorresponding drive.

Input device may include one or more conventional mechanisms that permita user to input information to the system, such as a keyboard, a mouse,a pen, a voice recognition device, etc. The output device may includeone or more conventional mechanisms that output information to the user,including a display, a printer, one or more speakers, or a medium, suchas a memory, or a magnetic or optical disk and a corresponding diskdrive. The communication interface may include any transceiver-likemechanism that enables system to communicate via a network and withother computers. For example, communication interface may include amodem, or an Ethernet interface for communicating via a local areanetwork (LAN). Alternatively, communication interface may include othermechanisms for communicating with other devices and/or systems viawired, wireless or optical connections.

The system 200 may perform such functions in response to processorexecuting sequences of instructions contained in a computer-readablemedium, such as, for example, memory, a magnetic disk or an opticaldisk. Such instructions may be read into memory from anothercomputer-readable medium, such as storage device or from a separatedevice via communication interface.

Any speech engine (ASR, TTS, NLU, or DM) is designed to work at acertain fixed operating point of its performance curve. Performance isusually specified in terms of “accuracy” (% correct) over a real-timefactor such as how much time the speech engine takes to complete a task.The designer of a speech system chooses an operating point for eachspeech engine employed in the system. This choice, once made, ultimatelydetermines how many simultaneous users can be handled per computingresource (CPU), given a specific application with a specific breakdownin terms of relative time (% of time) usage for each of the speechengines that deliver the service as a group (ASR, TTS, NLU, DM).

For example, a given application could have the average users spend 70%of time talking to the service (ASR), 20% listening to the service (TTS)and 10% waiting for system responses (NLU and DM). Given a target forthe peak number of users, which defines the peak system load, thedesigner then is able to calculate the number of servers or CPUs neededto carry the desired number of simultaneous users. The current inventionprovides voice-enabled applications with the ability to operate in adynamic mode which can improve the efficiency of the system and reducethe cost and expense in hardware.

According to one aspect of the invention, the specific systemconfigurations at the speech engine levels including the operation andfunction of all or any one of the ASR, NLU, DM and TTS modules may beautomatically and dynamically adjusted while deployed based on the givenapplication/task, and perhaps even on a call-by-call basis and on acaller basis. The adjustments are made based on internal or externalinformation, which may include, for example, CPU load, automatic numberidentification (ANI) information, customer profile, number of users,predictions of high usage due to sales or power outages or specialoffers, etc. The invention exploits the algorithmic choices that areinherent in each of the different kinds of speech engines. For example,by choosing a smaller beam width in searching candidates, the ASR andTTS modules can speed up their response time considerably with mostly asmall degradation in accuracy or synthesized voice quality. Thistrade-off in accuracy versus speed is done dynamically based on thevarious internal or external information fed to the system, such as theload requirements and accuracy expectations. In FIG. 2, the server 214is shown as the dynamical controller. This server receives the internaland external information and communicates via the network with thespeech engines on the various servers. The controller 214 will provideinstructions to one or more speech engine to modify its operationaccording to the external or internal information. The information maybe to adjust its operating point such that lower accuracy is achievedbut will allow for a higher user load. The instructions may also be toprovide higher accuracy with a lower user load.

Several examples illustrate this invention. One example may betime-based. If there is a time of day such as the evening or the weekendwhere usage is low, the controller may instruct the speech engines toincrease accuracy since there are fewer callers. During busy call times,the controller may instruct the speech engines to speed up operations(at a reduced accuracy) to manage the additional callers to the system.In another example, when the system is not working at its peak load, theconfigurations can be dynamically set so that maximum accuracy isobtained. Another example provides for adjusting the DM speech engine.Extreme loads could be handled by switching from a computationallyintensive mode of dialog such as a “how may I help you?”—type,user-initiative dialog, to a less demanding mode of machine directeddialog. This dynamic switch in operation mode could reduce computationalrequirements by a large factor. It is preferable that taking the drasticmeasure of switching dialog modes would occur when a threshold loadlevel is reached that is extremely high. In a more normal range ofoperation, trading off accuracy against speed of the speech engineswould suffice.

Implementing a load-scalable voice service requires a new softwaremodule for managing the load and also requires the speech engines to bealtered to enable scaling dynamically and to receive communications fromthe new controller module 214 to perform the scaling. The loadcontroller 214 will identify parameters for the speech engines such asthe appropriate operating points plus make appropriate choices regardingwhich dialog mode to use.

As can be appreciated, although the dynamic controller 214 is shown as aseparate server, there is no limitation regarding where this module runsand whether it is on a single server or on several servers.

Another aspect of the invention relates to adapting or modifying one ormore of the speech modules or algorithms and hardware resourcesaccording to determined information about an application or a particulartask. For example, a customer care application, a technical help deskapplication and a sales application may each need a different mix ofoperating points for resources. Since the same server farm may be usedfor several different applications, the resources may be reconfiguredfor the current application-based needs. Further, the resources may bereconfigured according to things occurring within an application, suchas depending on the turn of the dialog or the particular task (i.e., adifferent configuration for an initial welcoming portion of the dialogversus the portion of the dialog where account information is receivedor when the user is transferred to a human agent).

FIG. 3 illustrates a method embodiment of the invention. As shown inFIG. 3, the method comprises receiving information (302) and modifyingat least one module within a spoken dialog system according to thereceived information (304). The information may relate to theperformance of spoken dialog system, or may be internal or external tothe system. For example, the information may relate to a customerprofile and purchase history. Each caller may have a rating whichidentifies an operating point (quality of service, high or low accuracy,etc. for their call). The modification may, for example, improve theexperience for a particularly high-valued caller or more efficientlyutilize compute resources upon which the spoken dialog system runs. Thevarious types of information that may be received for the purpose ofadjusting one or more speech engines are set forth above. These include,for example, internal information such as CPU load, time-basedinformation such as it is the weekend which means a reduced callingload, historical information such as historically, after a power outage,we hit peak usage limits, external information such as data from themarketing department that a sale will begin next week and so forth. Whenthe adjustment are made on a call-by-call basis or based on ANI orcustomer profile information, the system may receive information thatthe customer purchases a lot of products from the company, and thereforethe system may automatically improve the accuracy of that call. Also asmentioned above, individual speech engines may be modified according tovarious parameters particularly associated with that speech engine orbased on a modification of the operating point for each speech engine.

Making the kind of dynamic adjustments and modifications to one or morespeech engines according to the principles of the invention may not onlyreduce the hardware costs associated with a spoken dialog system but mayalso improve revenue and efficiency by modifying the experience for highvalue customers and enabling the system to communicate with morecustomers simultaneously. Other benefits may be identified throughpractice of the invention.

Embodiments within the scope of the present invention may also includecomputer-readable media for carrying or having computer-executableinstructions or data structures stored thereon. Such computer-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer. By way of example, and notlimitation, such computer-readable media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to carryor store desired program code means in the form of computer-executableinstructions or data structures. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or combination thereof) to a computer, the computerproperly views the connection as a computer-readable medium. Thus, anysuch connection is properly termed a computer-readable medium.Combinations of the above should also be included within the scope ofthe computer-readable media.

Computer-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,objects, components, and data structures, etc. that perform particulartasks or implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Those of skill in the art will appreciate that other embodiments of theinvention may be practiced in network computing environments with manytypes of computer system configurations, including personal computers,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. Embodiments may also be practiced indistributed computing environments where tasks are performed by localand remote processing devices that are linked (either by hardwiredlinks, wireless links, or by a combination thereof) through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote memory storage devices.

Although the above description may contain specific details, they shouldnot be construed as limiting the claims in any way. Other configurationsof the described embodiments of the invention are part of the scope ofthis invention. Accordingly, the appended claims and their legalequivalents should only define the invention, rather than any specificexamples given.

1. A method comprising: receiving a time of day and an associatedprediction of call volume to a spoken dialog system based on a timelimited special offer; and modifying, via a processor, a dialog managermodule within the spoken dialog system according to the time of day andthe associated prediction of call volume, from a first dialog mode to asecond dialog mode, wherein modifying the dialog manager module resultsin a change of computational intensity for the spoken dialog system. 2.The method of claim 1, wherein modifying the dialog manager modulewithin the spoken dialog system further comprises modifying an automaticspeech recognition module, a natural language understanding module, anda text-to-speech module.
 3. The method of claim 2, further comprising:receiving a plurality of calls to the spoken dialog system, and whereinmodifying the dialog manager module within the spoken dialog systemoccurs on a call-by-call basis.
 4. The method of claim 2, whereinmodifying the dialog manager module within the spoken dialog systemfurther comprises modifying each of the automatic speech recognitionmodule, the natural language understanding module and the text-to-speechmodule according to parameters individually associated with eachrespective module.
 5. The method of claim 4, further comprising, whenboth the automatic speech recognition module and the text to speechmodule are modified, selecting a smaller beam width in searchingcandidates, wherein hardware requirements for the spoken dialog systemmay be reduced.
 6. The method of claim 1, wherein modifying the dialogmanager module within the spoken dialog system further comprisesmodifying an accuracy operating point of the dialog manager module. 7.The method of claim 1, wherein the change in computation intensityoccurs when a compute load level surpasses a predetermined threshold. 8.The method of claim 1, wherein modifying the dialog manager modulewithin the spoken dialog system further comprises modifying an accuracyof the dialog manager module to speed up a response time.
 9. The methodof claim 1, wherein modifying the dialog manager module within thespoken dialog system further comprises adjusting a predeterminedoperating point.
 10. The method of claim 1, wherein modifying the dialogmanager module more efficiently utilizes compute resources upon whichthe spoken dialog system runs.
 11. The method of claim 1, furthercomprising: receiving a plurality of calls to the spoken dialog system,and wherein modifying the dialog manager module is based on receivedinformation about a particular caller.
 12. A system comprising: aprocessor; and a computer-readable storage medium storing instructionswhich, when executed by the processor, cause the processor to perform amethod comprising: receiving a time of day and an associated predictionof call volume to the spoken dialog system based on a time limitedspecial offer; and modifying a dialog manager module within the spokendialog system, according to the time of day and the associatedprediction of call volume, from a first dialog mode to a second dialogmode, wherein modifying the dialog manager module results in a change ofcomputational intensity of the spoken dialog system.
 13. The system ofclaim 12, wherein the computer-readable storage medium stores additionalinstructions which result in the method further comprising: receiving aplurality of calls to the spoken dialog system, and wherein modifyingthe dialog manager module within the spoken dialog system occurs on acall-by-call basis.
 14. The system of claim 12, wherein modifying thedialog manager module further comprises modifying an accuracy operatingpoint of the dialog manager module.
 15. The system of claim 12, whereinthe computer-readable storage medium stores additional instructionswhich result in the method further comprising: receiving a plurality ofcalls to the spoken dialog system; and wherein modifying the dialogmanager module within the spoken dialog system is further based on arating assigned to each particular caller of the plurality of calls. 16.A non-transitory computer-readable medium storing instructions forcontrolling a computing device to perform a method comprising: receivinga time of day and an associated prediction of call volume to a spokendialog system based on a time limited special offer; and modifying adialog manager module within the spoken dialog system, according to thetime of day and the associated prediction of call volume received, froma first dialog mode to a second dialog mode, wherein modifying thedialog manager module results in a change of computational intensity forthe spoken dialog system.
 17. The non-transitory computer-readablestorage medium of claim 16, wherein the spoken dialog system isassociated with one of a customer care application, a technical helpdesk application and a sales application.
 18. The non-transitorycomputer-readable storage medium of claim 16, wherein modifying thedialog manager module within the spoken dialog system further comprisesmodifying a hardware resource.